{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-07T02:58:12.852944Z",
     "start_time": "2025-09-07T02:58:01.188816Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 读取Excel文件\n",
    "df = pd.read_excel(r'C:\\Users\\XLF\\OneDrive\\桌面\\src\\cumcm2025_-aproblem\\test\\log.xlsx')\n",
    "\n",
    "# 数据预处理\n",
    "# 1. 筛选出联合有效遮蔽时长大于0的数据（有效数据）\n",
    "df_filtered = df[df['联合有效遮蔽时长(s)'] > 0]\n",
    "\n",
    "# 2. 按迭代次数分组，找出每个迭代次数下的最优（最大）联合有效遮蔽时长\n",
    "optimal_df = df_filtered.groupby('迭代次数')['联合有效遮蔽时长(s)'].max().reset_index()\n",
    "\n",
    "# 3. 确保所有迭代次数都是连续的（从0到最大值）\n",
    "all_iterations = pd.DataFrame({'迭代次数': range(0, optimal_df['迭代次数'].max() + 1)})\n",
    "optimal_df = pd.merge(all_iterations, optimal_df, on='迭代次数', how='left')\n",
    "\n",
    "# 4. 填充NaN值（对于没有有效数据的迭代次数，使用前向填充，如果没有前值则用0）\n",
    "optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "\n",
    "# 创建图表\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# 绘制点线图\n",
    "plt.plot(optimal_df['迭代次数'], optimal_df['联合有效遮蔽时长(s)'],\n",
    "         marker='o', markersize=4, linewidth=1.5, color='#2E86AB')\n",
    "\n",
    "# 设置标题和标签\n",
    "plt.title('迭代次数与最优联合有效遮蔽时长的关系', fontsize=16, pad=20)\n",
    "plt.xlabel('迭代次数', fontsize=12)\n",
    "plt.ylabel('最优联合有效遮蔽时长(s)', fontsize=12)\n",
    "\n",
    "# 设置网格\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 优化布局\n",
    "plt.tight_layout()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 显示数据统计信息\n",
    "print(\"数据处理完成！\")\n",
    "print(f\"总迭代次数范围: 0 - {optimal_df['迭代次数'].max()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_df['联合有效遮蔽时长(s)'].min():.2f} - {optimal_df['联合有效遮蔽时长(s)'].max():.2f} s\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XLF\\AppData\\Local\\Temp\\ipykernel_34516\\1827015914.py:20: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ],
      "image/png": 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vvfeeGyKqfka///3v3fGlikUdH1dddZU7NrXfevuN9k1RNaH2w+a+k8BKQK+ySLMXfvXVV/5ZENUzzhump2N70aJFrn+Y9nNVrQVX8Gi2N1WGqXeZjkuvgkYVP3ov+mxUvSOqfNI+FPh7JVhr1W7aTy+55BJ/5ZBXPeRd9N2psk77q7Z/4MCBFg79ztl+++3dUGTtB6qcDLdXkjdkVcPmvCo2j9ah/U2fnz4rVUGpAjX4+9fnpL5foX63qSJQ35fea/DQRA1L1rq0fwfyhnPq81bVU3NU3aZqOVWEqepTVak6Fr0qTlUSar/RZ6rfJ9rvNJQTAJAGEp2KAQDSkzdblmbG0s+qIFI1jGbb0yx4+tn7V/JZs2a5fz1XVVHwv7qrciXwX9v1XM3c5/3rvSp0Amm9//znP91ju+66q2/06NGuIsZbXhUxe+65p6u2aM7nn3/un2HvoIMOajQ7mypITjjhBDdDmueBBx5w677hhhtCrk9VGdoOVYNpRiote+6557rKnb333ttVdbz00ktNKky8WbJUZdUSVVDouaogai99fvq82zOLn567atUq910H0sxc+ixVBeNVSmn2NV3ff//9rhpGF1V0qPJMP2s5VZR5j913333u+w2eFcyrlPL2B6/Ky6uUCqTZ9/TYRRddFPZMaPvtt597zu9///sWZ3HUd6mZxrSs3udf/vIXNxtda+69996IK1fef//9kJVk2l+0rwVWOnnVPPvvv79v6tSp7ucDDjjA9+mnnzaaLVP3q/opkI5BVW7pM9d3G1j5owqpSLY5sALKo3WqinDIkCHutY488khXXeXdr1n4Aj9zvYfu3bu7mQBVnaSLjisdh4EVd3q9rbbayn1O3kW3tQ+2RlV/obY1nEop7Z/nn3++L1I67rzqtWDevuf9brv22mtDruP00093j0dKFYN6z22l4y6carMZM2a0+TUAAKmJSikAQEKo19O0adP8t1X9s91227U4y5mqp0I17lVFi9crSRUeqsIoLi62kpISd5/6q6gyRb1s1APHa3Cuyg01TFZPJL22KkvUl6k1miVKVA3z0ksvuX/Z97ZbFQPqu6LXVlWCqI+QqD9UIJ1LqsG73pMqYNQfRv1wvKoINT5W/yGt33sv6r8TaOHChW67W6IqMFWWqB9RpDRzl2an86iyZqeddrJo2Hnnnd0sYd7npgoQVXzoO/QqmlSBFkwVFarUUWWM+japN5FXJRMN3v7RWjN5Vf1ov1UfMFVXqYm8qkxuueUWf2VNMFWVqOJNPczUM0fVdepBpQogfd+Bs5x5VPmjxtty7bXXtvpeNaOgeg+F6sejKiRVQ2kmwsBKIlXtqI+ZehWpok+fr44VHRuqHBSvqkW9zQJp/w6eHED7tJpXe/2ptIz2f+3n6u8UWO2jXkjqD6VKmuCG+bqtSi5th74XVWvpPn2OqqrU+9BrqNJKn6uqEjXznh5X9Vcw9fUKrHbT74Dg3lyBj+sY1THnHZet+emnn+zCCy9sNDvkDz/84K5VoaTfF56WvkdVTqqHmSqxvOO7tcbq2lY1FVcfKO2L6kEV3EtL+2wkkyREi77j1157zfXcU3WgKtHUB07VcarMUx857/tSpasq0VS5FTyhBACg4yGUAgAkhE44dOKo2eS84T4KkjQjlk6avEbCOrHXiZyGy2mYW+BwJ5246eKdMKphuAIBBUxqWKwTVm/oiKal17AZndx7gYZOvnUi3lZqeK4TYL1esMATdw0B00l/cBNunVzr+RpqoxM2hQQaqqSwRsP8NGucmqtr6JeafusEUye4OjlXaKWhOhqmppPQ5mhokYb36bNWgBMpDdsJDKU23XRTNyxI26Rm3Ar1FJR49P2pCbyGVTVHJ8barsDQRMGOQhCFUaE+T48CisBZ1GLBa4AfKiAKpH1IQZK+K+1fmnku1HC5ULRPqsG19lftH9onQ72ehukpzPGGvWlYYWvBqddwO3iImoaZamiXjo1x48Y1ekzN9DUMTvuYvnMNH9Xxo9DNG7aoIVYKWoKHiWoSAIVgOm61rAInbxu87dbQQPFCJ4U3GnIqGq4qen5wYKIQVE3NPYGzR3qN0gPDVw0T1Dq0fh1XCr71fvUZayKFwPXr94t+dwQO0dTvmMDgV7Nx6neP9s1whr9q31EAruPTC6W896zv0lu3Aicdx9rG4Abn3pA4PR44FLI1ej/6faLw8LHHHnPfsYYjBwb9+n2ZqObhXsCscFzHu/7xQMOPFc4G7vvaRs26qP3Ka5APAOi4CKUAAAnhBQ9eACAKnIL7MqkKQlUl3omc+rw0VzGgaimdcKlSKbAPkWbk0smd15NF1M9GfYRa6hPUHM2YpRNqzTCmPlCB1BNFFLp4FBqFCo60PQqdtKx+VpWHTsRUHaVZCUWzZ6lSSu9ZFTUKt3SSrKBo7ty57vNpqZeLTsK92dUioWoFhSHBAYRm19pll138oYdOqgNfX7d1v56nk/Hg2eOaoxNoBW6qnPAqc0JRlY+0tYJCFRgKjlRZ41GIpgohfVbeLIiiz9nre+TNTKd+UQo5RJU+moFO34/CIoUJqphSFYj2qVCVUjrhVtig8EffqQI9BS2h9kEFewo+td8rHFEvKIUsrc3ypopACV6nQk7tTwonDzvsMBckKVBTgKNKJ4U2CnU8qtBRUKUeQAo2VFWkzz2wV5YobPUCV81Yp1DKC8S8vmPerJReQKNjyJuxsiVegKLvzassOuGEE1w/LO3/oj5FCtL02Spg0n7rzbznvb7C1FAzP2p/9fZn73YgvRdVqik8URVSa7bccstGx77oGNfvnwceeMDNjudVjunzViWUKqsi8e9//9tdQtE+p/5WCrj1u1XrD6zOai6U0kyJ3iyDoWi/1nMDQ8FA+h2j/Vr7UeDnKcuWLXPHrY4nhbgKDzV7pI45/Z7QsaVtVTipfV3HlH636h8Q1INN/4Ch33sAgI6JUAoAkNQ09E4nnzoZ1omYTlyamz5d1QEKNNSIOVCoChRNBa+T8s8++8xVTEVCwYkCA51YqVooVGii9YZDlSiqmtGJvhqSKyRTdYkXhnhDX3QSriFOGhb1xhtvuFBKYYpEuv3h0NCoUNUo4Zwg66RYJ/LBgZ1H70MVcoFU/RN48twcBYnSXKPt1igY0Il3YFCn6hVVBelkXRdv+J4+Z1XqiNfMWfuSF0opSFRzc9F3qJPvSKgRu953cBCik3RtjwIFva72d61boZQqmnRpC4UyCni17+qiYaiqRPGGUKr5dCBVwYmqb1T5p+8skqb24gV8XoN/Vbipci+YgjyFesFBXuDEBl6Y4oXS3m0vAFOApmBXFTjB+3GoSQC8oYUtOf744916tW/qd4uqGKNBvzs0bE1DHBW+RFK9pIrPsWPHNrpPFYqBFG6rwkjVo6rQ8oYAat9ubgIHff76nIODKd2vsFEXhf2hHtexoYv2meBQSgG3QkOPXl/HnD5LhYYKolqiQE+N4fUPFuE2hQcApA5CKQBA0lI1i/oyaeiPTggVBulf0BXaqFIpOJhSsKNKlnCoGkmhlCpDIgl1vBP6UIGUaBieqKpEM/i1VMUken0FHzpRW758uXsPCrS8UEuVV6qSUYWYKIzSUDENw1KliapmFPJEm3cy39oMWKr8CB6+p/BCJ6eqqlD1mob7iU5a9VmH+tzCpRnsJLgPULj0WSvk0+erk3uvGkYXj6pitJ+pR5E3c2FrdMKsiphIqELGG2IaSN+thjSpmkpBq/ZVVYuIKoG8XmXN0eeuHj3N0T6k70Tr1c8K11TptO+++zZaTtV9CqN0rGlorYK85oLG5ug40PtQAKPjTceDepLp2FBFjI4lVa5pyJkCV4UV3lA/CQxA1CtLlWJexaN6yakCywsodewrDFVwqfVp31VVjo6t4CGPCvuaU983fB193to2hTvRCqV0nGuoqnrrKXxUFWS4NORQz2splBJVW2p4sgJuhfveMRhYMerR757mqEJMv+9EQ04DhzuGQ78j9L2pP52+L+0PCne9ykT1C9OshOrRp8d00e81LaOwV/+AoP1E/fN0TAIAOhZCKQBAUtK/yqsaQJUPGo7nNcNVUKWTZPVYUjgT6dA7j06uVaGiShGdtLU2Rbx3IqsgQSd1qnAINWxKJ/saPqQqm6uvvtptY0u83ks6eVflisINbziPAi5VOxx11FH+5TV0Sc2aFVaoSbpO2EJVjWl9V1xxhavA8Hp0tURVIxqeowoLhTJeU+vWprrX5xY4nMz7HFX5ctxxx7nQRZUtCj1UtaHX0LCltlAFjE5uFZQEN9tuKwUQ+t60fd62qw+P9jdVZ+hnfcat8Spd9J0pwGmJvhsNoWuuOkb9dvSdKrxra0VYa7SvqdJOgaaOMe1noapQtC+r75W2ScFOcHDVGn1f6rWlMETBqsIdhQ6q3FHwqaBZFVx33XWXC0Z0LOr1PIHbpHVpW719UgGHqnC8z0gVbAqOFEYpINSxpCFjeq/BQ369UEqhR2AwqNvBFVQ63g499NCw+4WFS6GRgrpbb73VBc9qdh/tHk6qdFQ1kjdM2Rs6Gi49TxMPaH9UiKtwVD2+IjmG9blpqKvWpe/YG8oZOCRY1VLaTzyzZs1y4aQq3PQPEtr3whkGDABIQYme/g8AkN7eeOMNNx34fffd579v7ty5vhEjRrj7//jHPzZaXlO/jxo1yj127LHHNpp+PtCll17qltFU5M3585//7Ja59957w9rWm2++2S1/3XXXNXmsrq7Ot88++/iys7N93333ne+KK65wyz722GNhrfvAAw/0derUyTdr1iz/fbvttptvvfXW861du9Z/X3V1tZueXctq/ZMnTw65vilTpviysrLcct26dfMVFRW1eunatavvhBNOcM/XZ5yfn+/eV3O0HaGmdtf9ni233NJ33nnnuZ81Zb1ut+btt99267n77rsb3a/16P6//vWv/vt0+6ijjmp1nbfddptb9vXXX/dNmjTJd/rpp7vb+r50XVNT45bTlPQ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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据处理完成！\n",
      "总迭代次数范围: 0 - 250\n",
      "最优联合有效遮蔽时长范围: 0.00 - 5.95 s\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T03:04:17.017387Z",
     "start_time": "2025-09-07T03:04:08.617745Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 读取Excel文件\n",
    "df = pd.read_excel(r'C:\\Users\\XLF\\OneDrive\\桌面\\src\\cumcm2025_-aproblem\\test\\log.xlsx')\n",
    "\n",
    "# 数据预处理\n",
    "# 1. 筛选出联合有效遮蔽时长大于0的数据（有效数据）\n",
    "df_filtered = df[df['联合有效遮蔽时长(s)'] > 0]\n",
    "\n",
    "# 2. 按迭代次数分组，找出每个迭代次数下的最优（最大）联合有效遮蔽时长\n",
    "optimal_df = df_filtered.groupby('迭代次数')['联合有效遮蔽时长(s)'].max().reset_index()\n",
    "\n",
    "# 3. 确保所有迭代次数都是连续的（从0到最大值）\n",
    "all_iterations = pd.DataFrame({'迭代次数': range(0, optimal_df['迭代次数'].max() + 1)})\n",
    "optimal_df = pd.merge(all_iterations, optimal_df, on='迭代次数', how='left')\n",
    "\n",
    "# 4. 填充NaN值（对于没有有效数据的迭代次数，使用前向填充，如果没有前值则用0）\n",
    "optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "\n",
    "# 创建图表\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# 绘制点线图\n",
    "plt.plot(optimal_df['迭代次数'], optimal_df['联合有效遮蔽时长(s)'],\n",
    "         marker='o', markersize=4, linewidth=1.5, color='#2E86AB')\n",
    "\n",
    "# 设置标题和标签\n",
    "plt.title('迭代次数与最优联合有效遮蔽时长的关系', fontsize=16, pad=20)\n",
    "plt.xlabel('迭代次数', fontsize=12)\n",
    "plt.ylabel('最优联合有效遮蔽时长(s)', fontsize=12)\n",
    "\n",
    "# 设置网格\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 优化布局\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('问题三联合有效遮蔽时长随迭代次数变化示意图.png', dpi=600, bbox_inches='tight')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 显示数据统计信息\n",
    "print(\"数据处理完成！\")\n",
    "print(f\"总迭代次数范围: 0 - {optimal_df['迭代次数'].max()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_df['联合有效遮蔽时长(s)'].min():.2f} - {optimal_df['联合有效遮蔽时长(s)'].max():.2f} s\")\n",
    "print(\"图片已保存为: 问题三联合有效遮蔽时长随迭代次数变化示意图.png\")"
   ],
   "id": "dfde7ecea6e31ed0",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XLF\\AppData\\Local\\Temp\\ipykernel_34516\\368801186.py:20: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据处理完成！\n",
      "总迭代次数范围: 0 - 250\n",
      "最优联合有效遮蔽时长范围: 0.00 - 5.95 s\n",
      "图片已保存为: 问题三联合有效遮蔽时长随迭代次数变化示意图.png\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T03:14:35.518642Z",
     "start_time": "2025-09-07T03:14:27.391806Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 读取Excel文件\n",
    "df = pd.read_excel(r'C:\\Users\\XLF\\OneDrive\\桌面\\src\\cumcm2025_-aproblem\\test\\log.xlsx')\n",
    "\n",
    "# 数据预处理\n",
    "# 1. 筛选出联合有效遮蔽时长大于0的数据（有效数据）\n",
    "df_filtered = df[df['联合有效遮蔽时长(s)'] > 0]\n",
    "\n",
    "# 2. 找出每个迭代次数下的最优（最大）联合有效遮蔽时长\n",
    "max_duration_per_iteration = df_filtered.groupby('迭代次数')['联合有效遮蔽时长(s)'].max().reset_index()\n",
    "\n",
    "# 3. 合并回原始数据，找出具有最优时长的所有行\n",
    "optimal_data = pd.merge(df_filtered, max_duration_per_iteration,\n",
    "                       on=['迭代次数', '联合有效遮蔽时长(s)'],\n",
    "                       how='inner')\n",
    "\n",
    "# 4. 为每个迭代次数的最优组添加标识\n",
    "# 由于同一迭代次数可能有多个组达到相同的最优时长，我们选择第一组\n",
    "optimal_data['group_id'] = optimal_data.groupby('迭代次数').cumcount() // 3\n",
    "first_optimal_groups = optimal_data.groupby(['迭代次数', 'group_id']).first().reset_index()\n",
    "optimal_data = optimal_data.merge(first_optimal_groups[['迭代次数', 'group_id']],\n",
    "                                 on=['迭代次数', 'group_id'],\n",
    "                                 how='inner')\n",
    "\n",
    "# 5. 确保每个迭代次数只有一组三个烟幕弹的数据\n",
    "optimal_data = optimal_data.sort_values(['迭代次数', '烟幕弹编号'])\n",
    "\n",
    "# 6. 创建新的数据表，包含所有需要的列\n",
    "columns_to_keep = [\n",
    "    '迭代次数', '烟幕弹编号', '无人机速度(m/s)', '飞行方向角(°)',\n",
    "    '投放延迟(s)', '起爆延迟(s)', '起爆时间(s)',\n",
    "    '投放点X(m)', '投放点Y(m)', '投放点Z(m)',\n",
    "    '起爆点X(m)', '起爆点Y(m)', '起爆点Z(m)',\n",
    "    '单枚有效遮蔽时长(s)', '联合有效遮蔽时长(s)'\n",
    "]\n",
    "\n",
    "optimal_data_table = optimal_data[columns_to_keep]\n",
    "\n",
    "# 7. 保存为新的Excel文件\n",
    "output_filename = '每代最优烟幕弹组合数据表.xlsx'\n",
    "optimal_data_table.to_excel(output_filename, index=False)\n",
    "\n",
    "print(\"数据处理完成！\")\n",
    "print(f\"已生成包含 {len(optimal_data_table)} 行数据的新表格\")\n",
    "print(f\"文件已保存为: {output_filename}\")\n",
    "\n",
    "# 显示一些统计信息\n",
    "print(f\"\\n统计信息:\")\n",
    "print(f\"总迭代次数: {optimal_data_table['迭代次数'].nunique()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_data_table['联合有效遮蔽时长(s)'].min():.2f} - {optimal_data_table['联合有效遮蔽时长(s)'].max():.2f} s\")\n",
    "print(f\"平均最优联合有效遮蔽时长: {optimal_data_table['联合有效遮蔽时长(s)'].mean():.2f} s\")"
   ],
   "id": "37f0370260e6474b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据处理完成！\n",
      "已生成包含 2802 行数据的新表格\n",
      "文件已保存为: 每代最优烟幕弹组合数据表.xlsx\n",
      "\n",
      "统计信息:\n",
      "总迭代次数: 243\n",
      "最优联合有效遮蔽时长范围: 2.26 - 5.95 s\n",
      "平均最优联合有效遮蔽时长: 5.49 s\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T03:20:40.925492Z",
     "start_time": "2025-09-07T03:20:31.694139Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 读取Excel文件\n",
    "df = pd.read_excel(r'C:\\Users\\XLF\\OneDrive\\桌面\\src\\cumcm2025_-aproblem\\test\\log.xlsx')\n",
    "\n",
    "# 数据预处理\n",
    "# 1. 筛选出联合有效遮蔽时长大于0的数据（有效数据）\n",
    "df_filtered = df[df['联合有效遮蔽时长(s)'] > 0]\n",
    "\n",
    "# 2. 按迭代次数分组，找出每个迭代次数下的最优（最大）联合有效遮蔽时长\n",
    "optimal_df = df_filtered.groupby('迭代次数')['联合有效遮蔽时长(s)'].max().reset_index()\n",
    "\n",
    "# 3. 确保所有迭代次数都是连续的（从0到最大值）\n",
    "all_iterations = pd.DataFrame({'迭代次数': range(0, optimal_df['迭代次数'].max() + 1)})\n",
    "optimal_df = pd.merge(all_iterations, optimal_df, on='迭代次数', how='left')\n",
    "\n",
    "# 4. 填充NaN值（对于没有有效数据的迭代次数，使用前向填充，如果没有前值则用0）\n",
    "optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "\n",
    "# 创建图表\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# 绘制点线图\n",
    "plt.plot(optimal_df['迭代次数'], optimal_df['联合有效遮蔽时长(s)'],\n",
    "         marker='o', markersize=4, linewidth=1.5, color='#2E86AB')\n",
    "\n",
    "# 设置标题和标签\n",
    "plt.title('迭代次数与最优联合有效遮蔽时长的关系', fontsize=16, pad=20)\n",
    "plt.xlabel('迭代次数', fontsize=12)\n",
    "plt.ylabel('最优联合有效遮蔽时长(s)', fontsize=12)\n",
    "\n",
    "# 设置网格\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 优化布局\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('问题三联合有效遮蔽时长随迭代次数变化示意图.png', dpi=600, bbox_inches='tight')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 显示数据统计信息\n",
    "print(\"数据处理完成！\")\n",
    "print(f\"总迭代次数范围: 0 - {optimal_df['迭代次数'].max()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_df['联合有效遮蔽时长(s)'].min():.2f} - {optimal_df['联合有效遮蔽时长(s)'].max():.2f} s\")\n",
    "print(\"图片已保存为: 问题三联合有效遮蔽时长随迭代次数变化示意图.png\")\n",
    "\n",
    "# 生成包含每个迭代次数中最优联合有效遮蔽时长的那一组三个烟幕弹数据的新表格\n",
    "# 1. 找出每个迭代次数下的最优（最大）联合有效遮蔽时长\n",
    "max_duration_per_iteration = df_filtered.groupby('迭代次数')['联合有效遮蔽时长(s)'].max().reset_index()\n",
    "\n",
    "# 2. 合并回原始数据，找出具有最优时长的所有行\n",
    "optimal_data = pd.merge(df_filtered, max_duration_per_iteration,\n",
    "                       on=['迭代次数', '联合有效遮蔽时长(s)'],\n",
    "                       how='inner')\n",
    "\n",
    "# 3. 为每个迭代次数的最优组添加标识\n",
    "# 由于同一迭代次数可能有多个组达到相同的最优时长，我们选择第一组\n",
    "optimal_data['group_id'] = optimal_data.groupby('迭代次数').cumcount() // 3\n",
    "first_optimal_groups = optimal_data.groupby(['迭代次数', 'group_id']).first().reset_index()\n",
    "optimal_data = optimal_data.merge(first_optimal_groups[['迭代次数', 'group_id']],\n",
    "                                 on=['迭代次数', 'group_id'],\n",
    "                                 how='inner')\n",
    "\n",
    "# 4. 确保每个迭代次数只有一组三个烟幕弹的数据\n",
    "optimal_data = optimal_data.sort_values(['迭代次数', '烟幕弹编号'])\n",
    "\n",
    "# 5. 创建新的数据表，包含所有需要的列\n",
    "columns_to_keep = [\n",
    "    '迭代次数', '烟幕弹编号', '无人机速度(m/s)', '飞行方向角(°)',\n",
    "    '投放延迟(s)', '起爆延迟(s)', '起爆时间(s)',\n",
    "    '投放点X(m)', '投放点Y(m)', '投放点Z(m)',\n",
    "    '起爆点X(m)', '起爆点Y(m)', '起爆点Z(m)',\n",
    "    '单枚有效遮蔽时长(s)', '联合有效遮蔽时长(s)'\n",
    "]\n",
    "\n",
    "optimal_data_table = optimal_data[columns_to_keep]\n",
    "\n",
    "# 6. 保存为新的Excel文件\n",
    "output_filename = '每代最优烟幕弹组合数据表1.xlsx'\n",
    "optimal_data_table.to_excel(output_filename, index=False)\n",
    "\n",
    "print(f\"\\n已生成包含 {len(optimal_data_table)} 行数据的新表格\")\n",
    "print(f\"文件已保存为: {output_filename}\")\n",
    "\n",
    "# 显示一些统计信息\n",
    "print(f\"\\n统计信息:\")\n",
    "print(f\"总迭代次数: {optimal_data_table['迭代次数'].nunique()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_data_table['联合有效遮蔽时长(s)'].min():.2f} - {optimal_data_table['联合有效遮蔽时长(s)'].max():.2f} s\")\n",
    "print(f\"平均最优联合有效遮蔽时长: {optimal_data_table['联合有效遮蔽时长(s)'].mean():.2f} s\")\n",
    "\n",
    "# 显示前几行数据作为示例\n",
    "print(f\"\\n前5组数据示例:\")\n",
    "print(optimal_data_table.head(15))  # 显示前5组数据（15行，每组3行）"
   ],
   "id": "9312284917885edb",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XLF\\AppData\\Local\\Temp\\ipykernel_34516\\1907655233.py:20: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据处理完成！\n",
      "总迭代次数范围: 0 - 250\n",
      "最优联合有效遮蔽时长范围: 0.00 - 5.95 s\n",
      "图片已保存为: 问题三联合有效遮蔽时长随迭代次数变化示意图.png\n",
      "\n",
      "已生成包含 2802 行数据的新表格\n",
      "文件已保存为: 每代最优烟幕弹组合数据表1.xlsx\n",
      "\n",
      "统计信息:\n",
      "总迭代次数: 243\n",
      "最优联合有效遮蔽时长范围: 2.26 - 5.95 s\n",
      "平均最优联合有效遮蔽时长: 5.49 s\n",
      "\n",
      "前5组数据示例:\n",
      "    迭代次数  烟幕弹编号  无人机速度(m/s)  飞行方向角(°)  投放延迟(s)  起爆延迟(s)  起爆时间(s)     投放点X(m)  \\\n",
      "0      8      1     96.5983  179.9561   1.1627   3.0749   4.2376  17687.6845   \n",
      "1      8      2     96.5983  179.9561   9.9413  15.8142  25.7555  16839.6847   \n",
      "2      8      3     96.5983  179.9561  36.2943   2.7300  39.0242  14294.0363   \n",
      "3      9      1     96.5983  179.9561   0.1000   2.7700   2.8700  17790.3402   \n",
      "4      9      2     96.5983  179.9561   9.9413  15.8142  25.7555  16839.6847   \n",
      "5      9      3     96.5983  179.9561  36.2943   2.7300  39.0242  14294.0363   \n",
      "6     10      1     96.5983  179.9561   0.1000   2.9264   3.0264  17790.3402   \n",
      "7     10      2     96.5983  179.9561   9.9413  15.8142  25.7555  16839.6847   \n",
      "8     10      3     96.5983  179.9561  45.0330   2.8511  47.8841  13449.8888   \n",
      "9     11      1     84.1766  179.9561   1.1627   3.3135   4.4762  17702.1273   \n",
      "10    11      2     84.1766  179.9561   2.1627  15.8142  17.9769  17617.9508   \n",
      "11    11      3     84.1766  179.9561  36.2943   2.7300  39.0242  14744.8722   \n",
      "12    12      1     96.5983  179.9561   1.1627   3.5741   4.7368  17687.6845   \n",
      "13    12      2     96.5983  179.9561   2.1627   0.9850   3.1477  17591.0863   \n",
      "14    12      3     96.5983  179.9561  22.5295   3.0377  25.5672  15623.6859   \n",
      "\n",
      "    投放点Y(m)  投放点Z(m)     起爆点X(m)  起爆点Y(m)    起爆点Z(m)  单枚有效遮蔽时长(s)  联合有效遮蔽时长(s)  \n",
      "0    0.0860     1800  17390.6547   0.3135  1753.6705         2.26         2.26  \n",
      "1    0.7354     1800  15312.0598   1.9053   574.5623         0.00         2.26  \n",
      "2    2.6850     1800  14030.3274   2.8869  1763.4820         0.00         2.26  \n",
      "3    0.0074     1800  17522.7653   0.2123  1762.4034         3.18         3.18  \n",
      "4    0.7354     1800  15312.0598   1.9053   574.5623         0.00         3.18  \n",
      "5    2.6850     1800  14030.3274   2.8869  1763.4820         0.00         3.18  \n",
      "6    0.0074     1800  17507.6535   0.2239  1758.0368         3.30         3.30  \n",
      "7    0.7354     1800  15312.0598   1.9053   574.5623         0.00         3.30  \n",
      "8    3.3314     1800  13174.4813   3.5424  1760.1701         0.00         3.30  \n",
      "9    0.0750     1800  17423.2105   0.2886  1746.2024         3.30         3.30  \n",
      "10   0.1394     1800  16286.7649   1.1589   574.5623         0.00         3.30  \n",
      "11   2.3397     1800  14515.0740   2.5157  1763.4820         0.00         3.30  \n",
      "12   0.0860     1800  17342.4317   0.3504  1737.4061         3.31         3.31  \n",
      "13   0.1600     1800  17495.9362   0.2329  1795.2458         0.00         3.31  \n",
      "14   1.6667     1800  15330.2535   1.8914  1754.7859         0.00         3.31  \n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T03:51:24.722433Z",
     "start_time": "2025-09-07T03:51:16.128707Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 读取Excel文件\n",
    "df = pd.read_excel(r'C:\\Users\\XLF\\OneDrive\\桌面\\src\\cumcm2025_-aproblem\\test\\log.xlsx')\n",
    "\n",
    "# 数据预处理\n",
    "# 1. 筛选出联合有效遮蔽时长大于0的数据（有效数据）\n",
    "df_filtered = df[df['联合有效遮蔽时长(s)'] > 0]\n",
    "\n",
    "# 2. 按迭代次数分组，找出每个迭代次数下的最优（最大）联合有效遮蔽时长\n",
    "optimal_df = df_filtered.groupby('迭代次数')['联合有效遮蔽时长(s)'].max().reset_index()\n",
    "\n",
    "# 3. 确保所有迭代次数都是连续的（从0到最大值）\n",
    "all_iterations = pd.DataFrame({'迭代次数': range(0, optimal_df['迭代次数'].max() + 1)})\n",
    "optimal_df = pd.merge(all_iterations, optimal_df, on='迭代次数', how='left')\n",
    "\n",
    "# 4. 填充NaN值（对于没有有效数据的迭代次数，使用前向填充，如果没有前值则用0）\n",
    "optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n",
    "\n",
    "# 将处理后的数据保存到新的Excel文件\n",
    "output_excel_path = '迭代次数与最优联合有效遮蔽时长.xlsx'\n",
    "optimal_df.to_excel(output_excel_path, index=False)\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "\n",
    "# 创建图表\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# 绘制点线图\n",
    "plt.plot(optimal_df['迭代次数'], optimal_df['联合有效遮蔽时长(s)'],\n",
    "         marker='o', markersize=4, linewidth=1.5, color='#2E86AB')\n",
    "\n",
    "# 设置标题和标签\n",
    "plt.title('迭代次数与最优联合有效遮蔽时长的关系', fontsize=16, pad=20)\n",
    "plt.xlabel('迭代次数', fontsize=12)\n",
    "plt.ylabel('最优联合有效遮蔽时长(s)', fontsize=12)\n",
    "\n",
    "# 设置网格\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 优化布局\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('问题三联合有效遮蔽时长随迭代次数变化示意图.png', dpi=600, bbox_inches='tight')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 显示数据统计信息\n",
    "print(\"数据处理完成！\")\n",
    "print(f\"总迭代次数范围: 0 - {optimal_df['迭代次数'].max()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_df['联合有效遮蔽时长(s)'].min():.2f} - {optimal_df['联合有效遮蔽时长(s)'].max():.2f} s\")\n",
    "print(f\"图片已保存为: 问题三联合有效遮蔽时长随迭代次数变化示意图.png\")\n",
    "print(f\"数据已保存为: {output_excel_path}\")"
   ],
   "id": "11d36c512b99f495",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XLF\\AppData\\Local\\Temp\\ipykernel_34516\\3670888258.py:20: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  optimal_df['联合有效遮蔽时长(s)'] = optimal_df['联合有效遮蔽时长(s)'].fillna(method='ffill').fillna(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据处理完成！\n",
      "总迭代次数范围: 0 - 250\n",
      "最优联合有效遮蔽时长范围: 0.00 - 5.95 s\n",
      "图片已保存为: 问题三联合有效遮蔽时长随迭代次数变化示意图.png\n",
      "数据已保存为: 迭代次数与最优联合有效遮蔽时长.xlsx\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-07T03:59:38.469816Z",
     "start_time": "2025-09-07T03:59:37.368971Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 读取之前生成的Excel文件\n",
    "optimal_df = pd.read_excel('迭代次数与最优联合有效遮蔽时长.xlsx')\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "\n",
    "# 创建图表\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# 绘制点线图\n",
    "plt.plot(optimal_df['迭代次数'], optimal_df['联合有效遮蔽时长(s)'],\n",
    "         marker='o', markersize=4, linewidth=1.5, color='#2E86AB')\n",
    "\n",
    "# 设置标题和标签\n",
    "plt.title('迭代次数与最优联合有效遮蔽时长的关系 (从保存数据重新生成)', fontsize=16, pad=20)\n",
    "plt.xlabel('迭代次数', fontsize=12)\n",
    "plt.ylabel('最优联合有效遮蔽时长(s)', fontsize=12)\n",
    "\n",
    "# 设置网格\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 优化布局\n",
    "plt.tight_layout()\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('从保存数据重新生成的图表.png', dpi=600, bbox_inches='tight')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 显示数据统计信息\n",
    "print(\"从保存数据重新生成图表完成！\")\n",
    "print(f\"总迭代次数范围: 0 - {optimal_df['迭代次数'].max()}\")\n",
    "print(f\"最优联合有效遮蔽时长范围: {optimal_df['联合有效遮蔽时长(s)'].min():.2f} - {optimal_df['联合有效遮蔽时长(s)'].max():.2f} s\")\n",
    "print(\"图片已保存为: 从保存数据重新生成的图表.png\")"
   ],
   "id": "5680d1eb99779b1f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "从保存数据重新生成图表完成！\n",
      "总迭代次数范围: 0 - 250\n",
      "最优联合有效遮蔽时长范围: 0.00 - 5.99 s\n",
      "图片已保存为: 从保存数据重新生成的图表.png\n"
     ]
    }
   ],
   "execution_count": 6
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
